Analysing arguments is a hard business. Throughout much of the 20th century many philosophers thought that formal logic was a key tool for understanding ordinary language arguments. They spent an enormous amount of time and energy teaching formal logic to students before a slow accumulation of evidence showed that they were wrong and, in particular, that students were little or no better at dealing with arguments after training in formal logic than before (e.g., Nisbett, et al., 1987). Beginning around 1960 a low-level rebellion began, leading to inter-related efforts in understanding and teaching critical thinking and informal logic (e.g., Toulmin, 1958).

Argument mapping has long been a part of this alternative program; indeed it predates it. The idea behind argument mapping is that while formal logic fails to capture much about ordinary argument that can help people’s understanding, another kind of syntax might: graphs. If the nodes of a graph represent the key propositions in an argument and arrows represent the main lines of support or critique, then we might take advantage of one of the really great tools of human reasoning, namely, our visual system. Perhaps the first systematic use of argument maps was due to Wigmore (1913). He presented legal arguments as trees, with premises leading to intermediate conclusions, and these to a final conclusion. This simple concept of a tree diagram representing an argument or subargument – possibly enhanced with elements for indicating confirmatory and disconfirmatory arguments and also whether lines of reasoning function as alternatives or conjunctively – has been shown to be remarkably effective in helping students to improve their argumentative skills (Alvarez, 2007).

However effective and useful argument maps have been shown to be, there is one central aspect of most arguments that they entirely ignore: degrees of support. In deductive logic there is no room for degrees of support: arguments are either valid or invalid; premises are simply true or false. While that suffices for an understanding of Aristotle’s syllogisms, it doesn’t provide an insightful account, say, of arguments about global warming and what we should do about it. Diagnoses of the environment, human diseases or the final trajectory of our universe are all uncertain, and arguments about them may be better or worse, raising or lowering support, but very few are simply definitive. An account of human argument which does not accommodate the idea that some of these arguments are better than the others and that all of them are better than the arguments of flat-earthers is one that is simply a failure. Argument mapping can not be the whole story.

Our counterproposal begins with causal Bayesian networks (CBNs). These are a proper subset of Bayesian networks, which have proved remarkably useful for decision support, reasoning under uncertainty and data mining (Pearl, 1988; Korb & Nicholson, 2010). CBNs apply a causal semantics to Bayesian networks: whereas BNs interpret an arc as representing a direct probabilistic dependency between variables, CBNs interpret an arc as representing both a direct probabilistic and a direct causal dependency, given the available variables (Handfield, et al., 2008). When arguments concern the state of a causal system, past, present or future, the right approach to argumentation is to bring to bear the best evidence about that state to produce the best posterior probability for it. When a CBN incorporates the major pieces of evidence and their causal relation to the hypothesis in question, that may already be sufficient argument for a technologist used to working with Bayesian networks. For the rest of us, however, there is still a large gap between a persuasive CBN and a persuasive argument. So, our argumentation theory ultimately will need to incorporate also a methodology for translating CBNs into a natural language argument directed at a target audience.

Example

Consider the following simple argument:

We believe that Smith murdered his wife. A large proportion of murdered wives turn out to have been murdered by their husbands. Indeed, Smith’s wife had previously reported to police that he had assaulted her, and many murderers of their wives have such a police record. Furthermore, Smith would have fled the scene in his own blue car, and a witness has testified that the car the murderer escaped in was blue.

Unlike many informal arguments, this one is already simple and clear: the conclusion is stated upfront, the arguments are clearly differentiated, and there is no irrelevant verbiage. Like most informal arguments, however, it is a probabilistic enthymeme: it supports the conclusion probabilistically rather than deductively and relies on unstated premises. So, it’s hard to give a precise evaluation of it until we make both probabilities and premises more explicit, and combine them appropriately.

The arrows indicate a direct causal influence of one variable on the probability distribution of the next variable. In this case, these are simple Boolean variables, and if one variable is true then this raises the probability that the next is true, e.g., if Smith did assault his wife, then this caused him to be more likely to murder his wife. (It could be that spousal assault and murder are actually correlated by common causes, but this wouldn’t alter the probabilistic relevance of assault to murder, so we can ignore the possibility here.)

First, we can do some research on crime statistics to find that 38% of murdered women were murdered by their intimate partners, and so get our probability prior to any other evidence.†

Second, we can establish that 30% of women murdered by their intimate partners had previously reported to police being assaulted by those partners (based upon Olding and Benny-Morrison, 2015). Admittedly, as O. J. Simpson’s lawyer argued, the vast majority of husbands who assault their wives do not go on to murder them. However, his lawyer was wrong to claim that Simpson’s assault record was therefore irrelevant! We just need to add some additional probabilities, which a CBN forces us to find, and combine them appropriately, which a CBN does for us automatically. Suppose that in the general population only 3% of women have made such reports to police, and this factor doesn’t alter their chance of being murdered by someone else (based on Klein, 2009). Then it turns out that the assault information raises the probability of Smith being the murderer from 38% to 86%.

Third, suppose we accept that if Smith did murder his wife, then the probability of him using his own blue car is 75–95%. Since this is imprecise, we can set it at 85% (say) and vary it later to see how much that affects the probability of the conclusion (in a form of sensitivity analysis).

Fourth, we can test our witness to see how accurate they are in identifying the color of the car in similar circumstances. When a blue car drives past, they successfully identify it as blue 80% of the time. Should we conclude that the probability that the car was blue is 80%? This would be an infamous example, due to Tversky and Kahneman, of the Base Rate Fallacy — i.e., ignoring prior probabilities. In fact, we also need to know how successfully the witness can identify non-blue cars as non-blue (say, 90%) and the base rate of blue cars in the population (say, 15%). Then it turns out that the witness testimony alone would raise the probability that Smith was the murderer from 38% to 69%. Combining the witness testimony with the assault information, then the updated probability that Smith is the murderer rises to 96%.

Even this toy example illustrates that building a CBN forces one to think about how the main factors are causally related and to investigate all the necessary probabilities. Assuming the CBN is correct for the variables considered, and is built in one of many good BN software tools, it acts as a useful calculator: it combines these probabilities appropriately to calculate the probability of our conclusion. Thus, it helps prevent much of the vagueness and fallacious reasoning that are widespread, even in important legal arguments.

Alternative Techniques for Argument Analysis

Although there are genuine difficulties in using this technique, we believe that much of the resistance to it is based on imaginary difficulties, while the (italicized) rival techniques below have difficulties of their own.

In our toy example, the prose version of the argument doesn’t quantify the probabilities involved, doesn’t specify the missing premises, doesn’t indicate how the various factors are related to each other, and it’s far from clear how to compute an appropriate probability for the conclusion. The fact that the probabilities and premises aren’t specified doesn’t really make the argument non-probabilistic, it just makes it vague. Prose is often the final form of presenting an argument, but it is far from ideal for the prior analysis of an argument.

Resorting to techniques from formal logic, diagrammatic or otherwise, requires even more effort than CBN analysis, while typically losing information. It is really appropriate only for the most rigorous possible examination of essentially deductive arguments.

A more recent approach with some promising empirical backing is the use of argument maps. These are typically un-parameterized non-causal tree structures in which the conclusion is the trunk and all branches represent lines of argument leading to it. (See Tim van Gelder’s ‘Critical Thinking on the Web’.) Arguably, these are equivalent to a restricted class of Bayesian network without explicit parameters (as in the qualitative probabilistic networks of Wellman, 1990). Thus, they have many of the advantages of BNs, but they don’t provide much guidance in computing probabilities, so they can be vague and subject to the kinds of fallacious reasoning that are avoided with actual BNs. Also, as they are typically not causal, they can actually encourage misunderstanding of the scenario.

Objections

There are many common objections to the use of Bayesian networks, or causal Bayesian networks, for argumentation. Here we address some of these.

1) Bayesian network tools are difficult to use.

This is true for those who are not experienced with them. “Fluency” with BN tools requires training something on the order of the amount of training required to become a reasonably good argument analyst using any tool. (In our experience, some philosophers get fed up with Bayesian network tools when they fail to represent an argument effectively within the first ten minutes of use!)

There are other options besides training. For specific applications, easy-to-use GUIs have been developed. Also, Bayesian network tools can be (and should be) enhanced to support features that would make them easier for argument analysis, such as allowing nodes to be displayed with the full wording of a proposition which they represent. But that’s up to tool developers. In the meantime, serious argument analysts would profit from learning how to use the tools, not just for the sake of argumentation, but also for the wide range of other tasks they have been developed for, such as decision analysis.

2) BNs force you to put in precise numbers for priors and likelihoods; this is a kind of false precision. Argument maps are better because they are qualitative.

Certainly, numbers need to be entered to use the automated updating via Bayes’ theorem. As quantities, they are precise (at least to whatever limited-precision arithmetic the tool supports). That doesn’t mean that the precision need be false, meaning falsely interpreted. The user can be fully aware of their limits. Indeed, all BN tools support sensitivity analysis, the ability to test the BN’s behavior across a range of values. So, if the analyst is unsure of just what the probability of something is, she or he can try out a range of numbers to see what effect the variation has on other variables of interest. If the conclusion can be substantially weakened by pushing the probability of premises around within reasonable limits, then it’s correct to infer that the argument is not compelling, and, otherwise, the argument may be compelling. This kind of investigation of the merits of the argument — and uncertainty of our beliefs — is not possible with qualitative maps alone.

Forcing one to obtain numbers is actually an advantage, as the example above indicated: the analyst is forced to learn enough about the domain to model it effectively.

3) Where do the numbers come from?

This is an objection any Bayesian will have encountered repeatedly. Since we are here talking about causal Bayesian networks, the ultimate basis for these probabilities must be physical dispositions of causal systems. Practically speaking, they will be sourced using the same means that Bayesian network modellers use in all the applied sciences, a combination of sample data (using data mining tools) and expert opinion (see Korb and Nicholson, 2010, Part III for an introduction to such techniques).

4) Naive Bayesian networks (NBNs) have been used effectively for argument analysis and are much simpler, e.g., by Peter Sturrock (2013) in his “AKA Shakespeare”. Why not just use them?

NBNs for argumentation simplify by requiring that pieces of evidence be independent of each other given one or another of the hypotheses at issue. If the problem really has that structure, then there’s nothing wrong with expressing it in an NBN. However, distorting arguments into that structure when they don’t fit causes problems, rather than resolving them. In Sturrock’s case, he suggested, for example, that the Stratford Shakespeare not having left behind a corpus of unpublished writing, not having written for aristocrats for pay, and not having engaged in extensive correspondence with contemporaries are all independent items of evidence, meaning that their joint likelihood is obtained by multiplying their likelihoods together (and then multiplied again with the likelihoods of all other items of evidence he advanced). The result was that he found that the probability that the writings of Shakespeare came from the eponymous guy from Stratford ranged from 10-15 all the way down to 10-21! As Neil Thomason pointed out to us, this means that you would be more likely to encounter the author of those works by randomly plucking any human off the planet at the time (or since!), rather than arranging to meet that Will Shakespeare from Stratford! While the simplicity of NBNs is appealing, this is a case of making our models simpler than possible. Real dependencies and interrelatedness of evidence cannot be ignored.

5) Some arguments are not about causal processes, but have a structure that can only be illuminated otherwise.

Here’s a famous case:

Socrates was a human.

All humans are mortal.

Therefore, Socrates was mortal.

While Bayesian networks can certainly represent deductive arguments, they will not be causal. Furthermore, their probabilistic updating will be uninformative. A reasonable conclusion is that BNs are ill suited for analysing deductive arguments. Argument maps may or may not be helpful; at least, their lack of quantitative representation will do no harm in such cases.

This concession is not exactly painful: our advocacy of CBNs was always only about cases where causal reasoning does figure in the assessment of a thesis. Slightly more problematic are cases where the core reasoning might be claimed to be associative rather than causal. For example, yellow stained fingers are associated with lung cancer, but staining your fingers yellow is not a leading cause of lung cancer. That implies we can make meaningful arguments from one outcome to the other without following a causal chain. (The inference of a causal chain from such associations is frequently derided as the “post hoc propter hoc” fallacy.)

In such cases, however, we are still reasoning causally, and it is best to have that causal reasoning made explicit:

Yellowfingers← Smoking → Lung Cancer

With the correct causal model, we can follow the dependencies, and we can also figure out the conditional independencies in the situation (screening off relations). Without the causal model available, we will only be using our intuitions to assess dependencies, and we will often get things wrong.

6) There are generally very many equally valid ways of modeling a causal system. How can one choose between them?

This is certainly correct. For example, between smoking and lung cancer there are a great many low-level causal processes required to damage lung cells and produce a malignant cancer. Whether we choose to model them or not depends upon our interests (pragmatics). If we are not arguing about the low-level processes, then we shall probably not bother to model them, as they would simply be a distraction. In general, there will always be multiple correct ways of modeling a causal system, meaning that the probabilistic (and causal) dependencies between the variables used are correctly represented. Which one you use will depend in part upon your argumentative purpose and in part upon your taste.

Argument Evaluation

If we are to know that our argument methods are good, we shall need methods of assessing them, built upon justifiable methods for assessing individual arguments. Arguments may be evaluated either as probabilistic predictions (if they are quantitative) or as natural language arguments or both. Here we will address quantitative evaluation. Evaluation of arguments in terms of their intelligibility, etc. we will leave to a future discussion.

One of the leading experts on probabilistic prediction in the social sciences, Philip Tetlock, has said “it really isn’t possible to measure the accuracy of probability judgment of an individual event” (Tetlock, 2015). This is not correct. To be sure, in context Tetlock points out that it is possible to measure the accuracy of probability judgments within a reference class, by accumulating the scores of individual predictions and using their average as a measure of judgment in like circumstances. Of course, if that is true, then such a measure applies equally to individual judgments within the reference class (one cannot accumulate the scores of individual predictions if there are no such scores!), so Tetlock’s point turns into the banal observation that you can “always” defend a failed probabilistic prediction. For example, if an event fails to occur that you have predicted with probability 99.9999%, you can shrug your shoulders and say “shit happens!” But actually that’s a defence that you cannot use too very often.

Tetlock suggests that the whole problem of assessing probabilistic predictions is a deep mystery. But his real problem is just the score he uses to assess predictions, namely the Brier score. It is a seriously defective measure of probabilistic predictions, and that ought to be surprising, since the real work in solving how to assess predictions was done half a century ago. But communications between the various sciences is slow and painful.

In most of statistical science an even worse measure of predictive adequacy is used: predictive accuracy. Predictive accuracy is defined as the number of correct predictions divided by the number of predictions. How can you do better in measuring predictive accuracy than using predictive accuracy? Of course, that’s why we slipped in the phrase “predictive adequacy” in place of “predictive accuracy”.

The problem with predictive accuracy is that it ignores the fact that prediction is inherently uncertain and so probabilistic. We should like our predicted probabilities to match the actual frequencies of outcomes that arise in similar circumstances. If, for example, we were using a true (stochastic) model to make our predictions, such a match would be guaranteed by the Law of Large Numbers. Predictive accuracy takes a probabilistic prediction’s modal value and effectively rounds it up to 1. For example, in measuring predictive accuracy, a probabilistic prediction that a mushroom is poisonous of 0.51 counts the same as one of 1. But that they should not be assessed as the same is obvious! The problem is what cognitive psychologists call “calibration”: if your probabilistic estimates match real frequencies on average, then you are well calibrated. Most of us are overconfident, pushing probabilities near 1 or 0 even nearer to 1 or 0. Nate Silver, for example, reports that events turning up 15% of the time are routinely said to be “impossible” (Silver, 2012). Another way of pointing this out is that predictive accuracy is not a strictly proper scoring rule, that is, it will reward the true probability distribution for events maximally, but it will also reward many incorrect distributions equally. For example, if you take every modal value and revise its probability to be maximal, you will have an incorrect distribution that is rewarded identically to the correct distribution.

Tetlock’s Brier score is strictly proper, but that doesn’t make it strictly correct. Propriety is a kind of minimum standard: if you can beat (or match) the truth with a false distribution, then the scoring function isn’t telling us what we want. Brier’s score reports the average squared deviation of the actual outcomes from the predicted outcome, so the goal is to minimize it (it is a form of root mean squared error). If we have the true distribution in hand, we cannot be beaten (any deviation from the actual probability will be punished over the long run). However, Brier’s score, while punishing deviant distributions, does so insufficiently in many cases. Consider the extreme case of predicting a mushroom’s edibility with probability 1. This will be punished when false with a penalty of 1. While such a penalty is maximal for a single prediction, in a long run of predictions, it may be washed out by other, better predictions. From a Bayesian point of view, this is highly irrational: a predicted probability of 1 corresponds to strictly infinite odds against any alternative occurring! That kind of bet is always irrational, and if it goes wrong, it should be punished by losing everything in the universe; that is, recovery should be impossible. The Brier score punishes mistakes in the range [0.9, 1] much the same, even though the shift from a prediction of 0.9 to 0.91 is qualitatively massively distinct from a shift from 0.99 to 1: a “step” from finite to infinite odds! Extreme probabilities need to be treated as extreme for a scoring function to correctly reward calibration and penalize miscalibration.

As we said, this problem has been solved some time ago, beginning with the work of Claude Shannon (Shannon and Weaver, 1949). Shannon proposed measuring information in a “message” by using an efficient code book to encode it and reporting the length of the encoding. An efficient code is one which allocates –log2 P(message) bits to all possible messages.

It turns out that log scores based upon Shannon’s information measure have all the properties we should like for scoring predictions. I.J. Good (1952) proposed as a score the number of bits required to encode the actual outcome given a Shannon efficient code based on the predicted outcome. That is, Good’s reward for binary predictions is:

This is the negation of the number of bits to report the actual outcome using the code efficient for the predictive distribution plus 1. The addition of 1 just renormalizes the score, so that 0 reports complete ignorance, positive numbers predictive ability above chance and negative numbers worse than chance, relative to a prior probability of 0.5 for a binomial event. Hope and Korb (2004) generalized Good’s score to multinomial predictions.

Nothing will be able to beat the true distribution in encoding actual outcomes with an efficient code over the long run; indeed, nothing will match it, so the score is strictly proper. But the penalty for mistakes is straightforwardly related to the odds one would take to bet against the winning proposition. Infinite odds imply an outcome that is impossible, meaning in information-theoretic terms, an infinite message describing the outcome. No matter how long a sequence of predictions is scored, an infinite penalty added to a finite number of successes will remain an infinite penalty. So, irrationality is appropriately punished.

All of this refers to the usual circumstance of scoring or assessing predictions, where we know the outcome, but we are uncertain of the processes which bring it about. Supposing that we actually know how the outcomes are produced is supposing that we have an omniscient, God-like perspective on reality. But, in fact, in special cases we do have a God-like perspective, namely when the events we are predicting are the outcomes of a computer simulation that we know, because we built it. In such cases, we can score our models more directly than by looking at their predictions and comparing them to outcomes. We can simply compare a model, produced, say, by some argumentative method, with the simulation directly. In that case, another information-theoretic construct recommends itself: cross entropy (or, Kullback-Leibler divergence). Cross entropy reports the expected number of bits required to efficiently encode an outcome from the true model (simulation, above) using the learned model instead of the true model. In other words, since we have both models (true and learned) we can compare their probability distributions directly, in information-theoretic terms, rather than taking a lengthy detour through their outcomes and predicted outcomes.

In Search of a Method

CBNs are an advantageous medium for addressing other common issues in argument analysis. Active open-mindedness suggests we can minimize confirmation bias by proactively searching out alternative points of view and arguments. This can be supported by constructing CBNs with sources of evidence and lines of causal influence additional to those which might at first satisfy us, and, in particular, which might be expected to cut against our first conclusion. In view of confirmation bias (and anchoring, etc.), it might be useful to give the task of constructing an alternative CBN to a second party.

Another benefit in using CBNs is the direct computational support for assessing the confirmatory power of different pieces of evidence relative to one another, how “diagnostic” evidence is in picking out one hypothesis amongst many. While Bayes’ factors— the relative likelihood of one hypothesis to another for the evidence — have long been recommended for assessing confirmation, once coded into a CBN the diagnostic merits of evidence for the hypotheses in play is trivially computable, and computed, by the CBN itself. Hence, the merits of each line of argument can be clearly and quickly assessed, whether in isolation or in any combination.

All of the above does not provide a complete theory of argumentation using CBNs. These uses of causal Bayesian networks must sit within a larger method. This must include deciding when CBNs are appropriate and effective, and when not. When they are not effective, alternative techniques will need to be applied, such as deductive logic or argument mapping. A rich theory of argumentative context and audience analysis is needed in order to understand such issues as which lines of argument can be left implicit (enthymematic) and which sources of premises are acceptable. And guidance needs to be developed in how to translate a CBN, which only represents arguments implicitly, into an explicit formulation in ordinary language.

The required techniques in which CBN-based argumentation is embedded are largely just those employed in critical thinking and argument analysis generally. It is a substantial, but achievable, research program, ranging across disciplines, to develop these to the point where trained analysts might produce similar, and similarly effective, arguments from the same starting points.

† The figure of 38% is a worldwide statistic from the WHO (“Domestic Violence”, Wikipedia). If the argument were specific to a country or region, other statistics might be more appropriate. The figure we have used is a reasonable one for the argument as stated, that is, without a specific context. Uncertainty for specific numbers can be treated via sensitivity analysis, as we discuss below.

In a recent blog post, Tim Wilson, the Australian Human Rights Commissioner, has defended Tony Abbott’s new rules restricting public servants in their political speech. In particular, he argues that it is not a genuine limitation of their speech and that it is a reasonable rule to impose on their employment. Here I will illustrate the process of argument analysis by a treatment of his argument. A prior caveat, however: there are always multiple, distinct ways of analysing arguments; and they will often be equally defensible. The goal of argument analysis is not to find a single, definitive argument which conclusively establishes a correct conclusion. (The plea for “proof” is a pretty good indicator of an absence of integrity in an argument!) The goal is to improve your argumentation and your thinking. Finality is a goal best reserved for the grave.

Tim Wilson’s Arguments

For the sake of brevity I will paraphrase Wilson’s arguments here. While excluding what is irrelevant to these two arguments in particular, the paraphrase is pretty accurate, as is easily determined by reference to the original. Also, I number the assertions and put them in blockquotes, although they are not literal quotes.

Argument 1: The New Rule Does Not Limit Free Speech

(1) The Department of Prime Minister and Cabinet has released new social media protocols. (2) The protocols limit the capacity of public servants to make political statements that are harsh or extreme in their criticism. (3) Employment codes are not law, and (4) so cannot constitute a legal limit on free speech. (5) Defending the universal human right of free speech is about the legal limits of speech.

Argument 2: The New Rule Is a Reasonable Employment Rule

(1) Codes of conduct provide an important civilizing role in filling gaps left by the law. For example, (2) codes of conduct restrict homophobic behavior. (3) Employment codes are not limiting, (4) since an employee may at any time resign. (5) What is specifically precluded by the new code is harsh and extreme criticism in areas that are related to their work.

I will apply the AA process only to the first argument, in order to keep this illustration of method reasonably short and clear.

Step 1: Clarify Meanings

Tim Wilson begins his post by pointing out that we should know something of what we talk about prior to opening our mouths: “Before anyone screams ‘free speech’, they should actually know what they are talking about.” The implied criticism of his critics, that they don’t know what they are talking about, is nowhere substantiated by Mr Wilson. However, the challenge is worth accepting.

So, what is free speech? Literally taken, it might be a right to say whatever you have the urge to say. In practice, however, as Wilson and every other commentator has noted, there are accepted limits upon speech. So, whatever right to speech we may be referring to is, and always has been, a limited right.

Freedom of speech as a right certainly has been recognized from long ago, for example, in the English Bill of Rights of 1689 and before that in ancient Greece, as John Milton noted in his famous defence of free speech, in Areopagitica. Free speech is recognized as fundamental in the Universal Declaration of Human Rights. It is notable also that the very first amendment in the Bill of Rights in the United States explicitly protects freedom of speech and a free press. Every democracy depends upon a free debate over public policy and principles, so attacks upon free speech are indirectly attacks upon democracy as well.

Nevertheless, it is perfectly well and widely accepted that there are proper limits on free speech. Speech that is likely to be hazardous or harmful to others is generally prohibited. Defamation and libel are also generally prohibited. And contracts may prohibit certain kinds of speech, such as the disclosure of proprietary information, as Wilson specifically notes. So, there is a real question whether Wilson’s defence of Abbott’s new rules is legitimate or not. Any reflex dismissal of it is a wrong reflex.

I have no particular unclarities about Wilson’s language, although I will return to some of the semantics later. I will also note that Wilson makes no distinction between “legal limits” on speech and “limits” on speech. That is, his post equivocates between them, attempting to support the claim that there are no limits imposed on free speech by Abbott’s actions because they do not impose any such limits in law. That inference is specious nonsense, of course.

Some Background

There is a relevant background to this issue. Tony Abbott and his government now have a track record of restricting freedom of speech and the flow of relevant public information in ways that at least suggest they fear public scrutiny of their actions. When the ABC reported on evidence of the mistreatment of refugees by the Royal Australian Navy, Abbott labeled them “Un-Australian”; many of his ministers also condemned the ABC, and they have suggested its funding and role should be curtailed. On any matters connected to dealing with refugees, Border Protection Minister Scott Morrison routinely invokes the cover of protecting military “operations” in refusing to address many questions, perhaps out of fear, for example, that smugglers might learn whether they have sent a boat to Australia. It seems likely that putting border protection and the handling of refugees under military control was, in part, designed to restrict public knowledge of the government’s activities. But, of course, issues of sovereignty and support for international law are pretty central to the public policy of a democracy. If anything is Un-Australian, it would have to be suppressing public debate about public policy.

Step 2: Identify Propositions

Already done.

Step 3: Graph the Argument

Argument 1 might be graphed as:

This shows its radical incompleteness. (1) is just setting context, identifying what protocols are at issue. The conclusion here is implicit, so the graph is quite fragmentary; the conclusion is in the argument’s title, so just numbering that (6) and making obvious connections we get a much better representation of the argument:

A few observations on graphing are in order. This graph is just a quick Google hack, but there are more sophisticated tools for the purpose, such as Austhink’s Rationale. That tool will give you some syntactic sugar that you may find useful; for example, it colors supporting links green and contrary arguments red. Here I’m inventing two small pieces of syntax: a dotted line for context setting that’s not really part of the argument; arrows joining together to show that a conjunction of premises is required for support. To be sure, (2) is also required for the inference to (6), but it is less closely associated with (4) and (5). If you have a disjunctive argument, such as “X or Y → Z”, you might want to show that clearly as well, using color or dotted lines, etc.

Step 4: Make it Valid

We now tackle the argument one subargument at a time. (3) → (4) is presumably not controversial, but it is certainly not, strictly speaking, valid. Dr Neil Thomason likes to invoke his “Rabbit Rule”: you can’t pull a rabbit out of a hat, unless it was already in there. The premise (3) doesn’t even mention limits or free speech, so it cannot be valid to conclude anything about them, as (4) does. What we need is some innocuous hidden premise to get us there, such as, (A) only laws can constitute legal limits on free speech. Since (A) is innocuous, this hasn’t revealed anything revelatory; but it is all part of the AA process.

(2) (4) (5) → (6) is much the bigger problem. First, let’s just look at (4) (5) → (6) in isolation. We have a Rabbit problem here as well: the conclusion says the new rules don’t limit free speech, whereas the premises are about legal limits only. This is not my artifact: the equivocation lies in the original, as you can see for yourself. We shall have to fix it, by some kind of bridge, that will allow a valid inference. A plausible candidate would be: (B) that which does not constitute a legal limit on free speech does not constitute a limit on free speech. From this it validly follows that there is no limit on free speech, given the premise that the new APS rules do not constitute a legal restriction on speech. There is, however, an immediate problem with (B), which is that it is obviously false. When you appear to be compelled to introduce an obvious falsehood as a missing premise, that tends to be a bad sign. There is no help to be found in Wilson’s post, since he there recognizes no distinction between legal and other limits on speech, sliding over any problem. This is where (2) comes in, at least in my thinking. It (and related text, that I have not copied) appear to be suggesting that employment codes can be legally relevant, in particular by violating the law. The laws that might be both relevant and violated here are not gone into, but the qualification that it is only harsh and extreme criticism that is being suppressed suggests some such qualification. Therefore, I shall adopt (B’) as the missing premise: (B) so long as it only limits harsh or extreme critical speech. The subargment in question then becomes (with some modest rephrasing):

(2) The new rules limit employees’ political speech that is harsh or extreme in its criticism. (3) Employment codes cannot constitute a legal limit on free speech, if they only limit harsh or extreme criticism. (5) Free speech is about the legal limits of speech. (B’) That which is not a legal limit on free speech also does not limit free speech, so long as it at most limits harsh or extreme critical speech. (6) Therefore, the new rules do not limit free speech.

Our graph at this point is:

I accept this as valid, or near enough, but that’s hardly the end of the story.

Step 5: Counterargue

Tim Wilson’s suggestion that the right to free speech only concerns limits in law is one key issue. This certainly does reflect, for example, the first amendment to the US Constitution, which restricts what laws the US Congress may make. It also reflects the underlying motivation for many declarations about human rights in general and free speech in particular; the underlying motivation is to not tolerate governments which attack such freedoms. What it does not reflect, however, is the ability of governments to attack freedoms indirectly and implicitly. A government may, for example, attack free speech by financing those who openly support its policies and deny financing to those who openly criticize its policies. While this may not violate explicitly the Universal Declaration of Human Rights, taken to an extreme it can be just as effective and pernicious as government actions which do openly violate that Declaration. More directly, “limiting free speech” is ordinary English, not legalese: Tim Wilson has neither the right nor the ability to arrogate its meaning for his own purposes. Telling people they cannot say something is limiting free speech, whatever pathetic spin Wilson cares to put on it. The only legitimate issue is whether the limitation is warranted or not, and on that count also Wilson is very much on the wrong side.

Wilson has gone to some pains to present his view as quite moderate. The only limitation of speech is that by an employment contract, and that speech must be extreme or harsh before any cause to dismiss can be found. So reads Wilson’s blog. And no ordinary person would expect to use extreme or harsh criticism of their employers in public and get away with it. Hence, the objectors must just be more of the chattering classes, of the latte-sipping variety. But there are a few points Wilson neglected, best considered with a latte in hand.

First of all, there is pre-existing policy that current APS employees might have a reasonable expectation of being enforced. The APS employment policy states:

It is quite acceptable for APS employees to participate in political activities as part of normal community affairs. APS employees may become members of or hold office in any political party.

Clearly, it follows from this that criticism of the existing government by opposition members who are a part of the public service is legitimate and protected, whether distributed via social media or otherwise. Of course, that does not mean that “harsh” or “extreme” criticism must be protected. Or, then again, perhaps it does. Presumably, since public servants are encouraged to run for public office, they are not meant to be severely handicapped relative to the incumbents they run against. But under the new Abbott rules that is the case: Abbott and other incumbents can be as obnoxious, harsh or extreme as they like in attacking their opponents, but if their opponents are also public servants, they cannot return in kind. If I were a public servant campaigning against the likes of Abbott, I would first resign. But that is irrelevant: the fact remains that Abbott’s rules clearly violate the intent of the existing code of conduct by restricting otherwise free political speech. Unfortunately, matters are even worse than what I have just written.

The exact wording of the new rules is, in fact, relevant. Specifically, they restrict opinions posted in social media, whether acting professionally or not, which are “so harsh or extreme in their criticism of the Government, Government policies, a member of parliament from another political party, or their respective policies, that they could raise questions about the employee’s capacity to work professionally, efficiently or impartially” (my emphasis). This covers, for example, scientist public servants who may want to raise questions about George Brandis’ preposterous declamations on the climate change debate. Oh my! Were I a public servant, perhaps I would be fired tomorrow for that last sentence! It is certainly true than I hold my current political masters in contempt! Nevertheless, the standard being set here for public servants being called to account is simply absurdly low. Under what circumstances can the pack of Brandis, Abbott, Morrison, Hockey, Turnbull and the rest possibly raise questions about the professionalism of those who oppose them? I will leave it to your imagination. But if you are a public servant, you will have no difficulty answering the question and keeping your mouth firmly shut. Which is just what your masters want.

Steps 6 and 7: Consider Alternatives and Evaluate

I will illustrate these steps in the negative, by omission. As pure pedagogy it is not necessary, since it repeats the first five steps on new arguments; as a positive example, it may be necessary. I plead my case as a matter of time: I’ve taken a fair bit to do this much and need to get to other things. Perhaps, in future I shall return to this and complete it, however. Also, perhaps reader comments will help fill the gap.

I will, however, quickly comment on Wilson’s second argument. Codes of conduct may either be civilizing or barbarous. This new code might count as civilizing were the enormous leeway in its interpretation taken away. Wilson’s implicit suggestion that they are limited to work matters is at best misleading, however, since both political campaigns and scientific publications are explicitly mentioned as being circumscribed by the new rules. That the rules do not take away an employee’s right to quit work and face unemployment hardly means that employees’ rights to free speech are thereby unimpaired. A kidnap victim’s “right” to refuse an order and thereby get shot in the head doesn’t make such an event the victim’s fault, nor does its availability restore the victim’s freedom. Abbott’s rules demonstrate, as if further demonstration were needed, that all of his impulses are against transparency and freedom of speech. Barbarity is the New World Order.

I had meant to deal with issues other than Australian politics in my early posts, however the events of recent days require some response. A key aspect of critical reasoning is openness to contrary opinion and a willingness to engage with evidence that undermines your own beliefs. This is one of the most difficult things for people to do. Some people do not even try.

Even more pathetic than these attempts to silence, not critics, but reporters reporting the news (and thereby pushing Australia closer to Egypt and Syria and away from its partner western democracies) are “confessions” of wrongdoing by the ABC itself and accusations of “over-reaching” by Media Watch’s Paul Barry, who said “Even if the police did back the asylum seekers’ claims, there was no way of knowing if they were true” (as reported in the Guardian). This last is a reprise of government’s claim that the ABC reporting allegations as allegations and not as facts is inappropriate. Presumably, allegations should only ever be reported after having been proven true. It’s understandable that a government whose central media mission appears to be to avoid media exposure should take this line, but it is preposterous and shameful that a TV program whose central mission is said to be to scrutinize the media should adopt it. The idea of BayesianWatch is that a Bayesian eye and brain can monitor public argument and critique it; the idea of Media Watch ought to be that an eye and brain monitor the public Media in Australia in the public’s interest, rather than provide cover for a government intent on suppressing media exposure of its actions.

In order for a free democracy to function effectively, evidence must be made available to the public. Evidence does not mean proof. Allegations are evidence: evidence of what people believe or, at least, of what they want you to believe that they believe. The video images of refugees’ burnt hands are evidence of their treatment. The precise nature of that treatment is not established by their burns or by their claims. But that doesn’t make the images any less evidence relevant to establishing how they were treated. Suppressing this evidence is well beyond any “mandate” of the Australian government, let alone that of Media Watch or the ABC itself. If the public is denied access to evidence of how the Australian government is pursuing its policies, then the democratic institutions in Australia will cease to have meaning. All those who love Australia and Australian democracy should insist that this disgusting behavior stop.

In this first substantive post I shall sketch out what I am after in general terms: the use of good arguments to further our understanding. Bad arguments dominate public debate. Most media commentators and politicians indulge regularly in them. How could they not? If there is a better understanding of what good and bad arguments are, then they might not, of their own volition, and the public might also compel them to lift their game.

Critical thinking1 is just as regularly endorsed as a central theme of education. Our students should leave school, or university, with the ability to think for themselves, with tools for critically analysing and assessing complicated arguments, and the ability to avoid being seduced by the many dread Fallacies. They are thought to be aided in this by being able to spot and identify examples of the many species of Fallacies. The result is the widespread abuse of people and their arguments for being “fallacious” — itself often a kind of argument ad baculum! (i.e., a form of bullying.) I shall have more to say about the fallacies on other occasions; indeed, since fallacies are frequently and abusively identified in perfectly good arguments, for the purpose of bad-mouthing them, I shall endeavour to unpick and expose many of them as perfectly good arguments in the future. But first we shall have to decide what good and bad arguments are.

Good Arguments

As Monty Python famously pronounced, “An argument is a connected series of statements intended to establish a proposition.” There’s not much dispute about that definition, but it tells us little about how to distinguish good from bad arguments. The traditional account of the goodness of arguments (the “alethic”, or truth-oriented, account) , taught for many years in philosophy departments, has been that good arguments are those with true premises and valid inferences (a “sound” argument). Validity refers to arguments that are so strong that their premises necessitate their conclusion: if their premises are true, it is impossible for their conclusions to be false. On this definition, a good argument is at least a pretty good thing — it guarantees that you arrive at the truth!2 It is not, however, good enough.

As Charles Hamblin, and others, pointed out there are pragmatic and rhetorical aspects to a good argument (Hamblin, 1970). An argument is not as good as possible if it fails to persuade its intended audience. While persuasive power is clearly insufficient as a mark of a good argument — at least so long as we refuse to acknowledge the arguments of Hitler and Mussolini as good — it is also necessary that they have some persuasive effect. Arguers need to attend to their audiences. Indeed, it is incumbent upon them to understand the cultural background and presuppositions of their audiences so as to frame their arguments. Amongst other things, arguments need to be grounded in premises that will be accepted by both parties. Good argument, therefore, necessarily engages one in considerations of practical psychology, sociology, culture and pragmatics, at least to some basic level. I will discuss some of these issues, including audience analysis, in future posts.

Another failing of the “true premises” test is that it is possible simply to luck into true premises, but intuitively good arguments are not lucked into. Instead we naturally expect good premises to be responsibly sourced. That is, either we obtain them from a recognized authority, or a reliable witness, or we have determined their truth for ourselves and can testify to them ourselves. This speaks to the normative side of judging arguments: they must be both persuasive in fact and rationally persuasive. The final ingredient is one already proposed in the alethic account, that of validity.

I suggest then that a good argument is: (1) one that persuades its target audience; (2) draws only upon acceptable premises — those that are themselves drawn from a reliable source; and (3) whose premises validly imply its conclusion.

I shall be demonstrating just how far short of this standard many of our political and public policy debates fall.

1 For more on critical thinking I strongly recommend the web pages of Tim van Gelder:

His critical thinking blog provides a great many useful explanations and provides links to a host of related resources around the web.

TvG’s argument mapping page describes the use of “maps” to understand arguments and leads to his computer program Rationale. (I’ve just noticed an argument mapping freeware alternative, Argumentative, at Sourceforge; I’ll have a look at it sometime.)

For reasons not entirely clear to me TvG has had more success than anyone else at teaching critical thinking. His advice on teaching it is worth a look, since it necessarily also provides good advice on how to do it.

In Bayesian Watch I provide techniques and examples of a Bayesian approach to argument analysis. The techniques are generic, incorporating ideas from critical thinking, informal logic, pragmatics, etc., commonly informed by ideas of Bayesian analysis and inference. The examples will be specific and will reflect my own interests, ranging from scientific method and statistics to politics and beyond. The examples will be for illustration, so that abstract ideas about argument analysis can be seen in live settings. But they will also often be meant to expose argumentative abuses, including many fallacies committed by our political leaders. For an introduction to Bayesian argument analysis, see a version of my paper Bayesian informal logic, published in Informal Logic.

The blog Bayesians without Borders (to which I contribute) explains what Bayesianism is and how it works, especially with respect to Bayesian network technology. For an illustration of Bayesian reasoning applied to argumentation you can look at my analysis of the Sally Clark case there.